STUDENTS' SATISFACTION TOWARDS THE IMPLEMENTATION OF BLENDED LEARNING: A CASE STUDY IN UITM NEGERI SEMBILAN - IAR JOURNAL

 
International Academic Research Journal of Social Science 5(1) 2019 Page 1-14

Students’ Satisfaction Towards the Implementation of Blended Learning: A Case Study
                              in UiTM Negeri Sembilan
   Saliza Ramly1, Sri Yusmawati Mohd Yunus1, Rozianiwati Yusof1, Normaziah Abdul Rahman1, Nor Azlina Aziz
                                      Fadzillah1, Shamsudin Md Sarif1

                                     1Universiti   Teknologi MARA Negeri Sembilan
     sallyza@ns.uitm.edu.my, sriyusmawati@ns.uitm.edu.my, rozian696@ns.uitm.edu.my, maziah@ns.uitm.edu.my,
                              norazlina@ns.uitm.edu.my, shamsudinms@ns.uitm.edu.my

      Article Information                                                Abstract

      Keywords                           Blended Learning (BL) is the way e-learning is being combined with
      Blended Learning (BL),             traditional classroom methods. It is becoming a flexible and effective
      Learning Management System
      (LMS), Satisfaction                platform in teaching-learning environment especially in the higher-level
                                         institution. Universiti Teknologi MARA (UiTM) has launched its Learning
                                         Management System (LMS) to facilitate an online teaching and learning. BL
                                         was introduced in 2011 to several courses in UiTM. Currently, BL is
                                         commonly used as the method in teaching and learning in most of the UiTM
                                         courses. However, it is crucial in knowing whether this method is applicable
                                         and significant to students in process of teaching and learning in UiTM
                                         Negeri Sembilan. The purpose of this study is to identify the students’
                                         satisfaction towards the implementation of BL in UiTM Negeri Sembilan
                                         based on five satisfaction domains which are interaction, instruction,
                                         instructor, course managements and technology. This study covered two
                                         campuses in UiTM Negeri Sembilan which involve four faculties; Faculty of
                                         Computer and Mathematical Sciences (FSKM), Faculty of Sports and
                                         Recreation (FSR), Faculty of Applied Science (FSG) and Faculty of
                                         Administrative Science and Policy Studies (FSPPP). Data was gathered
                                         through an online survey distributed to students. Multiple Linear Regression
                                         and Multivariate Analysis of Variance (MANOVA) were used to further
                                         analyse the data. Based on result, it shows that students had satisfied with
                                         the implementation of BL.

      INTRODUCTION
      Universiti Teknologi MARA (UiTM) has officially launched its Learning Management System (LMS) called i-
      Learn portal in December 2005. At the beginning, i-Learn portal acts as a medium to support teaching and
      learning processes in UiTM. It is a convenient way to communicate between lecturers and students other than
      just a face-to-face communication in a formal class. In 2011, Blended Learning (BL) was introduced involving
      lecturers and students from all over UiTM in Malaysia. i-Learn portal has become an official LMS to support
      BL in UiTM.

      UiTM Negeri Sembilan is one of the UiTM branch that used BL as the method of teaching and learning. A study
      conducted by Isnania Z.M. et al. (2015) showed that lecturers have implemented BL method in UiTM Negeri
      Sembilan because they believed the factors of students’ understanding, system applications and materials play
      important roles in the effective process of teaching and learning. Lecturers also have been given training in

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many Web 2.0 tools in order to help them preparing materials for online learning. Ramly S. et al. (2016) have
investigated the usefulness of BL in UiTM Negeri Sembilan. Based on the study, 82.14% lecturers preferred to
implement BL. It is confirmed that the BL method is very useful because the lecturers have the facilities and
skills to support BL.

The rapid development of technology has led to the succession of BL. Nowadays, students are preferred to use
technology in their process of learning. Study done by Taib J.M., Ramly S., Ramli R. (2016) revealed that
students often use web services that support learning activities to communication with lecturers and friends.
There are several web services has been used by students in UiTM Negeri Sembilan to communicate such as
Whatsapp, i-Learn portal, facebook, and email. However, i-Learn portal as official LMS remains an important
platform to support BL in UiTM. It is also considered as the private cloud computing which allows lecturers for
distributing of course content, communication, collaboration, content management and assessment. There are
several features in i-Learn portal that helps to support BL in UiTM such as course content, course forum, online
assessment, assignment, chat, and video. These features are important to support the interaction between
lecturers and students.

LITERATURE REVIEW
Blended Learning (BL) has been introduced since 1990. According to Owston et al. (2013), BL is a process of
online learning that is a part of the formal teaching and learning in class which is combines e-learning and
traditional face-to-face learning. According to Afip (2014), BL is defined as a learning process that combines
methods of teaching, delivery, media structures or the inclusion of all methods in one process. BL has been
improvised to increase the communication between students and lecturers inside and outside the classroom.
Nowadays, BL also involves learning tools to help student to understand about the course that they took better.
These tools have helped students to do more exercises online and also have helped them give better responds to
the lecturers. Furthermore, students learn to adapt with BL since they can get access to the internet through their
smartphone.

Recently, BL techniques become more widely employed in this learning model, learners not only can access the
learning material but also they be able to communicate with both lecturers and other students in a distant mode.
In the learning paradigm that focuses on combining and optimizing both conventional and e-learning advantages
and potentials while terminating both learning models challenges and shortages, is often apply the term blended
(Kintu, et. al.).

BL was introduced in 2011 to several courses in UiTM. Currently, BL is commonly used as the method in
teaching and learning in most of the UiTM courses. BL in UiTM refers to integrating the face to face as
traditional learning and the e-learning courses through Learning Management System (LMS) to achieve more
efficient teaching and learning process. Thus, to solve the problems of time and room constraint in UiTM. Based
on Sary & Tsuyoshi (2017), BL in developed countries are more common than similar studies in developing
countries, especially about students’ performance and preference in learning. Moreover, one of the most
important issues in BL is how satisfy this approach towards students and lecturers.

A study conducted by Ramly S. et. al (2016) showed that lecturers agreed that BL helped in solving the
insufficient classroom and time to meet the students in UiTM Negeri Sembilan. In UiTM Negeri Sembilan,
there are numbers of courses that have been blended as noted in course syllabus. The lecture time for face to
face (in classroom) and non-face to face (online learning) is scheduled properly to make sure BL run smoothly.
This approach has given the solution to the constraint of time and venue.

Since BL has already been introduced in UiTM Negeri Sembilan, it is important to know whether this method is
applicable and significant to students in process of teaching and learning in UiTM Negeri Sembilan. This study
will be focused on how effective the BL in satisfied the students from the perspective of student satisfaction
based on five elements which are interaction, instruction, instructor, course managements and technology. The
selected elements are as a result of the past researches and the researcher’s experience at UiTM Negeri Sembilan.

Interaction
BL has been a helpful ‘tool’ to support both teaching and learning because this method can be used to allow
students to interact using technologies, not only can they cooperate in formal classes but can also be useful
outside the classroom. For example, through BL students can have discussions, and also ask questions to their
lecturers if they do not understand about a certain topic. BL also allows students to communicate and do the
assignments given, during and after formal class sessions depending on the task given by the lecturers for each
course.

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According to Akkoyunlu and Soylu (2008), BL was delivered through combining web-based and traditional
learning. Interaction commonly happened through face to face, however, the web based approached also
efficient in conducting the interaction between students with lecturers and students with other students. The
web-based was used to download exercises and assignments, however with the improvement of web technology
enable interaction between other users. For example, forum for discussion. The forum helped to increase
students’ interaction with other students and lecturers. In this forum, lecturers also helped in answering and
responding to students’ questions and also evaluate the students’ performances in the process of BL. Students’
interaction and communication are not limited only in formal classes because BL process requires both students
and lecturers to communicate well in order to accomplish the given tasks. The implementation of BL system has
provided lecturers and students with educational technologies, more flexible instructional methods, and various
learning sources practiced in an interactive learning environment which have helped to reduce the limitations of
classroom and limited time in a formal class (Shmais & Adas, 2011).

Instruction
Instruction is another domain that is supposed to use to measure the students’ satisfaction towards the
implementation of BL. There are many differences between BL instructions and traditional instructions.
However, each type of learning has its own advantages and disadvantages. It is important to investigate the
effectiveness of BL instruction in terms of students’ satisfaction (Giannousi M. et al., 2009). From the past
researches, BL instructions is more efficient than tradition instructions although it requires extra time and effort
to create the instruction.

By implementing BL instruction, all assessments, course content and resources are available on and via online.
It can be the medium where the lecturer can perform some discussion besides being a place to post notes, and
references with active links as a class activity and assessment to make fully utilize the discussion and reducing
face-to-face confrontation. In addition, Hung and Chou, (2015) noted that that system satisfaction and
multimedia instruction could positively not only instructors but also to students’ attitude toward and enjoyment
of BL. However, according to Naaj, Nachouki, and Ankit (2012), feedbacks given by the instructor is the most
important aspect in students’ satisfaction with the instruction. BL approached gives advantages since it
combined face-to-face interaction and web-based interaction which lecturers mostly give instruction in class, the
implementation of BL helped in terms of giving instructions because not only the lecturers are able to give away
tasks and assignments in class, the lecturers can also give instruction outside the formal class session. Besides
that, according to Harandi (2015), with the advancement of technology used, instruction had been made more
interesting for the students since it can be delivered in different media such as video, audio and text. The
instruction can be more clear delivered and understood by the students easily.

Instructor
Another important domain to measure students’ satisfaction towards implementation of BL is the instructor.
Level of guidance of the instructor towards students will be considered in rating their satisfaction. Research
done by So and Brush (2008) has explained in detail where the evaluation will be based on how the instructor
delivers the content knowledge, provides appropriate scaffolding, clarifies misunderstanding, and increase
students’ motivation. They said that the instructors’ modelling and scaffolding of social presence behavior is
important because it might be needed for students who are distance learners.

Instructors and instructional designers play a key role since they are the ones who are responsible as to where
they have to invest more time and effort on the analysis of learners, learning contents, contexts, and
technologies to design an effective BL course. By using BL many activities can be done by the instructors for
example like live chat session, online discussions, forums, quizzes, and assignments and with these kind of
systems, instructional delivery and communication between instructors and students can be performed at the
same time (synchronously) or at different times (asynchronously). Wu, Tennyson and Hsia (2010) found that
this system can provide instructors and students with multiple, flexible instructional methods, and educational
technologies.

However, instructors should also provide sufficient incentives and administrative support to encourage students
to actively participate in BL courses and to ensure computer literacy between all students in order to utilize all
the technology used in communicate with the instructors. Besides that, they also should cultivate a positive
interaction publicly to increase participant communication and collaboration learning via the system (Wu,
Tennyson, & Hsia, 2010). In addition, instructors need to deal with the large numbers of students which is
difficult for them to spend more time and effort to follow up with all students from every class (Shaqour, 2014).

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Course Managements
Blended e-learning system use different methods such as tutorials and online discussions as a platform for
sharing and delivering course content (So & Brush, 2008). Wu, Tennyson and Hsia (2010) suggested that
blended e-learning system should consider a relevant system and content element that include multimedia
presentation and flexibility. Besides, good class management gives high satisfaction in BL (Naaj, Nachouki, &
Ankit, 2012). There are many other systems or resources that are available in BL environment such as e-mail,
live chat sessions, online discussions, forums, quizzes and assignments. These systems provide good
communication between students and instructors even though they are afar (Pituch & Lee, 2006).

Technology
Technology also contributes to the student’s satisfaction towards BL. Smart and Cappel (2006) found that the
more experience students have with technology, the more positive they are towards BL. Technology is one of
the factors in BLES environment that contributes to the student’s satisfaction towards BL (Wu et al., 2008).
Furthermore, good experience with technology also leads to higher level of user’s satisfaction in BL (Simmers
& Anandarajan, 2001; Volery & Lord, 2000). The use of technology has brought a positive impact on the BL
environment (Taradi K. et al., 2005). Problems with technology weaken the course management system
(Giannousi M. et al., 2009). Besides, Popovici and Mironov (2015) stated that students were aware of the
changes brought by technology and they implement technology to support their learning process.

METHODOLOGY
The study of student satisfaction towards the implementation of BL measured the five elements which are
interaction, instruction, instructor, course managements and technology. This study focusses on i-Learn portal as
official LMS. This study covered two campuses in UiTM Negeri Sembilan; Seremban and Kuala Pilah
campuses which involve four faculties; Faculty of Computer and Mathematical Sciences (FSKM), Faculty of
Sports and Recreation (FSR), Faculty of Applied Science (FSG) and Faculty of Administrative Science and
Policy Studies (FSPPP). The study also includes students diploma and degree level.

Research Instrument
Data was gathered through questionnaire online survey distributed among all students in UiTM Negeri Sembilan.
The questionnaire was divided into seven sections. In Section A, the demographic profile was asked based on
required information which are gender, age, type of faculty, type of program, semester, and time spend using BL
(in a month). Section B, C, D, E and F asks the questions on the five domains which are interaction, instruction,
instructor, course management and technology respectively. In these five sections, the students were required to
answer based on a 5-point Likert Scale, ranging from 1= Strongly Disagree until 5= Strongly Agree. And the
last section which is section G are questions about the students’ suggestion on BL and the student’s overall
satisfaction towards the implementation of BL. For the overall satisfaction, students were required to answer
based on a 10-point Likert Scale, ranging from 1=Very Dissatisfied to 10=Very Satisfied.

Research Population
The respondents in this study were the students from UiTM Negeri Sembilan. There are two campuses in UiTM
Negeri Sembilan, which are UiTM Seremban 3 campus and UiTM Kuala Pilah campus. The figure 1 shows the
number of students both in bachelor’s degree and diploma from four faculties; Faculty of Science Computer and
Mathematics (FSKM), Faculty of Sports Science and Recreation (FSR), Faculty of Administrative Science and
Policy Studies (FSPPP) and Faculty of Applied Science (FSG). The size of the sample of the study can
determine the level of variability in the population itself. Based on the rule of thumb from Roscoe (1975),
sample size that is more than 30 respondents will be appropriate especially in conducting inferential analysis or
else also known as statistical analysis to achieve certain objectives.

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                                                      Fig. 1.
                                 Population of Students in UiTM Negeri Sembilan

Data Analysis
This study used two types of data analysis which are descriptive analysis and inferential analysis. Inferential
analysis is used when statistical technique is applied in the research. There are two types of inferential analysis
used in this study which are multiple linear regression (MLR) analysis and multivariate analysis of variance
(MANOVA).

However, the reliability test under scale measurement should be done to determine the reliability of the survey
forms. Coefficient Alpha or Cronbach’s Alpha was used to measure the concept of consistency reliability. The
coefficient varies from 0 to 1. The level of internal consistency of Cronbach’s alpha value is acceptable within
0.5 to 0.7 and shows a good level if it is more than 0.7 (Nunally and Bernstein, 1994; Streiner and Norman,
2008).

Meanwhile, descriptive analysis involves the transformation of raw data into a form that could provide
information to describe a set of factors in a situation. To perform descriptive analysis, the demographic profiles
were obtained which includes gender, education levels, types of faculty, time spend using BL (spending in a
month).

  Multiple Linear Regression Analysis
  Hair et al. (2010) stated that a multiple linear regression analysis is a statistical technique that can be used to
  analyze the relationship between a single dependent (criterion) variable and several independent variable
  (predictor) variables. In this study, the dependancy between interaction, instruction, instructor, course
  management and technology with the students’ satisfaction has been determined towards the implementation
  of BL. Each independent variable was weighted by the regression analysis procedure to ensure the maximum
  prediction from the set of predictor variables.

   Multivariate Analysis of Variance (MANOVA)
  Multivariate analysis of variance (MANOVA) is an extension of analysis of variance when study consists of
  more than one dependent variable (Grimm and Yarnold, 1995). For this case, the comparison of students’
  satisfaction domains has been done which it involves student from different faculties. The process of
  MANOVA starts with a focus on the categorical groups and determines how means of several dependent
  variables differ (Harlow and Duerr, 2013). According to Johnson and Wichern (2007), the statistical model
  for multivariate analysis of variance is:

                   where            are independent Np (0,) variables
                                    is an overall mean (level)
                                    represents the th treatment effects with = 0

RESULT AND DISCUSSION
In this study, 245 students of UiTM Negeri Sembilan which is 62 students (25.3%) taking diploma and 183
students (74.7%) taking bachelor’s degree. It consists of 66 male students (26.9%) and 179 female students
(73.1%). There are 77 students (31.4%) from Faculty of Computer Science and Mathematics (FSKM), 71
students (29.0%) from Faculty of Sport Science and Recreation (FSR), 58 students (23.7%) from Faculty of

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Administrative Science and Policy Studies (FSPPP), 39 students (15.9%) from Faculty of Applied Sciences
(FSG).

In a month of studies, there are 142 students (58.0%) spend in range of one to three times using BL and 63
students (25.7%) spend time in a range of four to six times. Furthermore, it shows that 40 students (16.3%) in
UiTM Negeri Sembilan uses BL in a range of seven and above in a month. There are two statistical techniques
used in this study which are Multiple Linear Regression and Multivariate Analysis of Variance (MANOVA).

In the questionnaire, five sections represent for five domains which are interaction, instruction, instructor,
course management and technology respectively (Appendix). As can be seen in the summarized table 1,
Interaction 6 has the highest mean score which is 3.46. This indicates that students are satisfied with their
participation in the class during BL session. In the instruction section, Instruction 1 has the highest mean score
which is 3.72. This indicates that students are satisfied with the use of BL technology in this course that
encourages them to learn independently. In Instructor section, Instructor 6 has the highest mean score which is
3.72. This indicates that students are satisfied with the amount of time given by the instructor (lecturer) to
complete the given assignments. In terms of course management, Course Management 2 has the highest mean
score which is 4.01. This indicates that students are satisfied when their lecturer/supervisor always takes
attendance. In terms of technology, Technology 5 has the highest mean score which is 3.67. This indicates that
students are satisfied with technology is a faster and better access to information.

                                                       TABLE 1
                                     THE HIGHEST MEAN OF FIVE SATISFACTION DOMAINS

                                          Satisfaction Domains            Mean
                                      Interaction 6                        3.46
                                      Instruction 1                        3.72
                                      Instructor 6                         3.72
                                      Course Management 2                  4.01
                                      Technology 5                         3.67

The calculation of mean score obtained from this study explained all the dimensions that needed to be
investigated. Compute mean analysis which comes with the SPSS was used to locate the mean score of the
level of students’ satisfaction towards the implementation of BL.

Multiple Linear Regression (MLR)
MLR is used to define the significant relationship between satisfaction domains and the students’ satisfaction
towards implementation of BL. Before conducting MLR, there are some major assumptions that need to be
fulfilled in order to continue with the analysis.

  i.     Assumptions of Multiple Linear Regression Analysis
             i) Normality of the error term distributions
             ii) Linearity of the relationship between independent variables and the dependent variables.
             iii) Constant variance of the error terms (Homoscedasticity)
             iv) Independence of the error terms (Autocorrelation)

  ii.    Significance of the overall model: Testing the coefficient of determination

                                                     TABLE 2
                               R VALUE, R SQUARE AND STANDARD ERROR OF THE ESTIMATE

                               R            R Square        Standard Error of the Estimate
                             0.964             0.930                    0.296

         From the table 2, it shows the value of R Square which is 0.930, it can be concluded that there is 93.0%
         of the students’ satisfaction in implementation of BL is explained by the five satisfaction domains and
         the balance of 7.0% is explained by other factors. The R value is 0.964 which indicates that there is a
         strong positive linear relationship between the five satisfaction domains with the students’ satisfaction
         towards the implementation of BL. Therefore, the test of significance of the model is conducted to
         determine the overall fit of the model.

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                                                       TABLE 3
                                             F-VALUE AND SIGNIFICANT VALUE

                                         F                  Significant Value
                                       632.116                     0.000

         From the table 3, since the significant value is 0.000 is less than alpha = 0.01, the model is said to be a
         good fit for the data.

  iii.   Significance test of regression coefficients
         Since the model is a good fit for the data, each satisfaction domain is used to test whether it is
         significant in explaining the students’ satisfaction towards the implementation of BL. From the test, all
         the satisfaction domains (interaction score, instruction score, instructor score, course management
         score and technology score) are significant explaining the students’ satisfaction since all the p-values
         are 0.000 and it is less than alpha = 0.01.

  iv.    The regression coefficients

                                                      TABLE 4
                                  REGRESSION COEFFICIENT OF INDEPENDENT VARIABLES

                                             Model                 Coefficients
                                  Constant                            0.047
                                  Interaction Score                   0.071
                                  Instruction Score                   0.066
                                  Instructor Score                    0.074
                                  Course Management Score             0.065
                                  Technology Score                    0.066

         From the table 4, the estimated regression equation obtained is:

         Where:
             Y: Students’ Satisfaction Towards Implementation of BL
             X1: Interaction Score
             X2: Instruction Score
             X3: Instructor Score
             X4: Course Management Score
             X5: Technology Score

         In terms of interaction score, it can be said that for every 1 unit increase of interaction score, students’
         satisfaction towards implementation of BL will be increased by 0.071. Other factors are assumed to be
         fixed. In terms of instruction score, it can be said that for every 1 unit increase of instruction score,
         students’ satisfaction towards implementation of BL will be increased by 0.066. Other factors are
         assumed to be fixed. In terms of instructor score, it can be said that for every 1 unit increase of
         instructor score, students’ satisfaction towards implementation of BL will be increased by 0.074. Other
         factors are assumed to be fixed. In terms of course management score, it can be said that for every 1
         unit increase of course management score, students’ satisfaction towards implementation of BL will be
         increased by 0.065. Other factors are assumed to be fixed. In terms of technology score, it can be said
         that for every 1 unit increase of technology score, students’ satisfaction towards implementation of BL
         will be increased by 0.066. Other factors are assumed to be fixed.

Multivariate Analysis of Variance (MANOVA)
MANOVA was performed to identify if there is a significant difference between students’ satisfaction domains
among faculties in UiTM Negeri Sembilan towards the implementation of BL. The dependent variables are
interaction scores, instruction scores, instructor scores, course management scores and technology scores. The
groups used to compare the difference in the mean scores between groups are faculties in UiTM Negeri
Sembilan. Before conducting the analysis, two major assumptions needed to be fulfilled which are normality
and homogeneity of variance-covariance matrices.

  i.     Assumptions of MANOVA
         i) Normality Checking
             To fulfil one of the assumptions of multivariate normality in MANOVA, the researchers have
             checked both univariate normality and multivariate normality. For univariate normality, Normal

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          Q-Q plot for each dependent variable was constructed to make sure this assumption was not
          violated. From the Normal Q-Q plot for each satisfaction domain, the points are plotted
          approximately on the straight line. This indicates that the scores for each dependent variable is
          reasonably normally distributed (Table 5).

                                                  TABLE 5
                                             MAHALANOBIS DISTANCE

                             Distance                  Minimum                  Maximum
                      Mahalanobis Distance                   0.497                 31.374

          To check the multivariate normality, Mahalanobis distances was produced and compared against
          the critical value obtained from the chi-square critical value table (Table 6). The comparison was
          made between the maximum value of Mahalanobis distance (31.374) and the critical value
          (15.0863) with 5 number of dependent variables as the degree of freedom (df), and alpha level of
          0.01. Since the maximum value of Mahalanobis distance is greater than the critical value,
          suggesting the presence of multivariate outliers.

                                                      TABLE 6
                                                 CHI-SQUARE VALUE

                            Number of Dependent Variables                 Critical Value
                                             5                                 15.0863

      ii) Homogeneity of Variance-Covariance Matrices
          Since the data is reasonably multivariate normally distributed, the researcher continues to test for
          the second assumption in MANOVA, which is equality of covariance matrices. Box’s Test of
          equality of covariance matrices was conducted to see whether the data violates the assumption of
          homogeneity of variance-covariance matrices (Table 7).

                                                  TABLE 7
                               BOX’S TEST OF EQUALITY OF COVARIANCE MATRICES

                                                                     Value
                                 Box’s M                             71.989
                                 F                                   1.533
                                 Significant Value                   0.012

           From the significant value of Box’s M, it shows that 0.012 is approximately equal to the alpha
           level of 0.01 suggesting that the assumption of equality of covariance matrices is not violated. It
           can be concluded that the covariance matrices of the four faculties in UiTM Negeri Sembilan are
           the same. Based on the multivariate normality and equality of covariance matrices, both
           assumptions are not violated. Therefore, the researcher continued with multivariate analysis of
           variance (MANOVA).

ii.   Multivariate Test
      For the multivariate test, the researcher used Wilk’s Lambda to test whether there are statistically
      significant differences among four faculties in UiTM Negeri Sembilan on the satisfaction domains
      (interaction scores, instruction scores, instructor scores, course management scores, and technology
      scores).

                                                     TABLE 8
                                                 MULTIVARIATE TEST

            Effect           Statistic               Value               F               Significant Value
            Faculty       Wilk’s Lambda              0.843             2.999                   0.001

      From the table 8, the value of Wilk’s Lambda is 0.843, with the significant value of 0.001 which is less
      than alpha level of 0.01. Therefore, there is a significant difference on the satisfaction domains among
      faculties in UiTM Negeri Sembilan towards the implementation of BL. However, this result does not
      show which satisfaction domain is having statistically significant different for the mean scores among
      four faculties in UiTM Negeri Sembilan.

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To analyse the result, the researcher will compare the mean scores for all faculties according to each
satisfaction domain. This will show that for each satisfaction domain, which faculty significantly
differs in the mean scores for their satisfactions towards the implementation of BL. This is done by
looking at the significant value and comparing them with an alpha level of 0.01. If the significant value
is less than an alpha level of 0.01, it indicates that there is a significant difference for the mean scores
of the dependent variables between FSKM, FSR, FSG, and FSPPP.

                                               TABLE 9
                          PAIRWISE COMPARISON OF STUDENTS’ INTERACTION SCORE

          Faculty (I)      Faculty (J)     Mean Difference (I-J)      Significant Value
                          FSR                      -1.077                      0.572
          FSKM            FSPPP                     2.091                      0.016
                          FSG                       0.189                      1.000
                          FSKM                      1.077                      0.572
          FSR             FSPPP                     3.168                      0.000
                          FSG                       1.266                      0.621
                          FSKM                     -2.091                      0.016
          FSPPP           FSR                      -3.168                      0.000
                          FSG                      -1.902                      0.122
                          FSKM                     -0.189                      1.000
          FSG             FSR                      -1.266                      0.621
                          FSPPP                     1.902                      0.122

From the table 9, only FSR and FSPPP shows significant difference in the interaction scores with
significant value, 0.000 which is less than alpha level of 0.01. This result shows that, for interaction
scores for students’ satisfaction towards implementation of BL, only FSR is significantly different in
the mean scores with FSPPP. The mean scores for FSR is 3.168 higher than FSPPP. This stated that
students in FSR are more satisfy with the interaction in BL compared to students in FSPPP. However,
there is no significant difference in the mean scores of interaction between students in FSKM with FSR,
FSPPP, and FSG. It also shows that there is no significant difference in the mean scores of interaction
between students in FSG with FSR and FSPPP. The student’s mean scores of interaction between these
three faculties are equal.
                                              TABLE 10
                          PAIRWISE COMPARISON OF STUDENTS’ INSTRUCTION SCORE

           Faculty (I)       Faculty (J)    Mean Difference (I-J)     Significant Value
                           FSR                       -0.918                    0.779
           FSKM            FSPPP                      1.855                    0.027
                           FSG                        0.174                    1.000
                           FSKM                       0.918                    0.779
           FSR             FSPPP                      2.773                    0.000
                           FSG                        1.092                    0.807
                           FSKM                      -1.855                    0.027
           FSPPP           FSR                       -2.773                    0.000
                           FSG                       -1.618                    0.173
                           FSKM                      -0.174                    1.000
           FSG             FSR                       -1.092                    0.807
                           FSPPP                      1.618                    0.173

From the table 10, only FSR and FSPPP show a significant difference in the instruction scores with the
significant value 0.000 which is less than alpha level of 0.01. In this result, it shows that there is a
significant difference in the mean scores of instruction between FSR and FSPPP. The mean instruction
scores for FSR is 2.773 higher than FSPPP. This stated that students in FSR are more satisfied with the
instruction in BL compared to students in FSPPP. However, there is no significant difference in the
mean scores of instruction between students in FSKM with FSR, FSPPP, and FSG. It also shows that
there is no significant difference in the mean scores of instruction between students in FSG with FSR
and FSPPP. The student’s mean scores of instruction between these three faculties are equal.

                                              TABLE 11
                          PAIRWISE COMPARISON OF STUDENTS’ INSTRUCTOR SCORE

            Faculty (I)      Faculty (J)   Mean Difference (I-J)     Significant Value
                            FSR                     -0.060                     1.000
            FSKM            FSPPP                    2.078                     0.001
                            FSG                      0.621                     1.000
            FSR             FSKM                     0.060                     1.000

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                               FSPPP                    2.138                   0.001
                               FSG                      0.682                   1.000
                               FSKM                    -2.078                   0.001
           FSPPP               FSR                     -2.138                   0.001
                               FSG                     -1.456                   0.165
                               FSKM                    -0.621                   1.000
           FSG                 FSR                     -0.682                   1.000
                               FSPPP                    1.456                   0.165

From the table 11, there is a significant difference between FSKM and FSPPP in terms of mean
instructor scores with a significant value 0.001 which is less than alpha value 0.01. There is also a
significant difference between FSR and FSPPP in terms of instructor scores with significant value
0.001 which is less than alpha value 0.01. For FSKM students, the mean instructor score is 2.078
higher than FSPPP students. This stated that students in FSKM are more satisfied with the instructor in
BL compared to students in FSPPP. Besides that, the mean instructor scores for FSR is 2.138 higher
than FSPPP students which also means that FSR students are more satisfy with the instructor in BL
environment. However, there is no significant difference in the mean instructor scores for students in
FSG and FSPPP. It shows that both FSG and FSPPP students obtained equal mean scores for instructor
in BL environment.

                                              TABLE 12
                     PAIRWISE COMPARISON OF STUDENTS’ COURSE MANAGEMENT SCORE

            Faculty (I)        Faculty (J)     Mean Difference (I-J)      Significant Value
                               FSR                     -0.678                   0.991
           FSKM                FSPPP                    1.984                   0.001
                               FSG                     -0.428                   1.000
                               FSKM                     0.678                   0.991
           FSR                 FSPPP                    2.662                   0.000
                               FSG                      0.250                   1.000
                               FSKM                    -1.984                   0.001
           FSPPP               FSR                     -2.662                   0.000
                               FSG                     -2.412                   0.001
                               FSKM                     0.428                   1.000
           FSG                 FSR                     -0.250                   1.000
                               FSPPP                    2.412                   0.001

From the table 12, there is significant difference between FSKM and FSPPP in terms of Course
Management scores with significant value 0.001 which is less than alpha value 0.01. Moreover, there is
a significant difference between FSR and FSPPP with significant value 0.000 which is less than alpha
value 0.01. There is also a significant difference between FSG and FSPPP in terms of Course
Management scores with significant value 0.001 which is less than alpha value 0.01. The mean course
management scores for FSKM students is 1.984 higher than FSPPP students. Meanwhile, the mean
course management scores for FSR students are 2.662 higher than FSPPP students. From the results, it
also shows that the mean course management scores for students in FSG is 2.412 higher than FSPPP.
Therefore, the students in FSKM, FSR and FSG are more satisfy with implementation of BL in terms
of course management compared to FSPPP. Moreover, the result shows that there is no significant
difference in the mean course management scores for students in FSKM, FSR and FSG.

                                              TABLE 13
                          PAIRWISE COMPARISON OF STUDENTS’ TECHNOLOGY SCORE
                 Faculty (I)     Faculty (J)    Mean Difference (I-J)   Significant Value
                                FSR                   -0.171                  1.000
               FSKM             FSPPP                  2.167                  0.000
                                FSG                    0.079                  1.000
                                FSKM                   0.171                  1.000
               FSR              FSPPP                  2.338                  0.000
                                FSG                    0.250                  1.000
                                FSKM                  -2.167                  0.000
               FSPPP            FSR                   -2.338                  0.000
                                FSG                   -2.008                  0.007
                                FSKM                  -0.079                  1.000
               FSG              FSR                   -0.250                  1.000
                                FSPPP                  2.088                  0.007

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         From the table 13, there is a significant difference between FSKM and FSPPP, FSR and FSPPP in
         terms of Technology scores with significant value 0.000 which is less than alpha value 0.01. There is
         also a significant difference between FSG and FSPPP in terms of Technology scores with a significant
         value of 0.001 which is less than the alpha value 0.01. The mean technology scores for students in
         FSKM are 2.167 higher than FSPPP which indicates that the students in FSKM are more satisfy with
         the technology used in BL compared to FSPPP. From this result, it also shows that FSR students are
         more satisfied with technology used in BL compared to FSPPP (2.338 higher than FSPPP) and students
         in FSG have higher mean technology scores (2.088) than FSPPP. For the mean technology scores
         between FSKM, FSR and FSG, there is no significant difference which indicates that the students’
         satisfaction scores in terms of technology used in BL are the same for the three faculties.

CONCLUSION AND FUTURE WORK
The study examined the students’ satisfaction towards the implementation of BL in UiTM Negeri Sembilan.
There are five domains have been used to shows students’ satisfaction which includes interaction, instruction,
instructor, course management and technology. Based on the results, it shows that, students are satisfied with the
quality of interaction and instruction used by the lecturers to conduct BL session. They agreed that certain
courses offered in BL session together with traditional learning method can improve teaching and learning
method in UiTM Negeri Sembilan. However, based on previous study by Naaj (2012), even though the students
were satisfied with the implementation of BL, yet they still preferred conventional learning. Moreover, the
student’s satisfaction towards the implementation of BL are different among the faculties in UiTM Negeri
Sembilan. For interaction and instruction, the result shows that only students in FSR and FSPPP differ in terms
of their satisfaction towards implementation of BL, while students in FSKM have the same satisfaction towards
the implementation of BL with FSR, FSG and FSPPP. Furthermore, students in FSKM and FSR are more
satisfied with the implementation of BL in terms of instructor compared to FSPPP students. In addition,
students in FSKM, FSR and FSG are more satisfied in terms of course management and technology compared to
FSPPP. In conclusion, among the four faculties in UiTM Negeri Sembilan, only FSPPP students differ for their
satisfaction towards the implementation of BL with the other faculties in the mean scores for interaction,
instruction, instructor, course management and technology.

BL is commonly used in education especially in higher level of education. Further research is needed to improve
the use of BL in higher education towards students’ satisfaction. Therefore, more sample data are required for
each faculty to represent the whole population in UiTM Negeri Sembilan. Besides five domains, other factors
should be considered to measure the students’ satisfaction towards BL. In addition, sample students’ data can be
collected from other campuses or universities in Malaysia. In future work, it is recommended to compare BL
with traditional learning among students and to focus on the preferable types of BL for certain courses.

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APPENDIX

Section: Interaction

This section will ask you about your opinion on how you satisfy with the implementation of BL in terms of
interaction.

                                                                          Strongly                                Strongly
Satisfaction Domain                                                                  Disagree   Neutral   Agree
                                                                          Disagree                                 Agree
Interaction                                                                  1          2         3         4        5
A blended learning session keeps me always alert and focused.
Interaction is able to maintain with the lecturer even not in class
session.
A blended learning course makes it more important for students to visit
the lecturer during office hours.
I am satisfied with the quality of interaction between students and
lecturer in blended learning session.
I am satisfied with the way I interact with other students in blended
learning session.
I am satisfied with my participation in the class in blended learning
session.

Section: Instruction

This section will ask you about your opinion on how you satisfy with the implementation of BL in terms of
instruction.

                                                                          Strongly                                Strongly
Satisfaction Domain                                                                  Disagree   Neutral   Agree
                                                                          Disagree                                 Agree
Instruction                                                                  1          2         3         4        5
The use of blended learning technology in this course encourages me to
learn independently.

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My performance in exams is improved when using BL compared to
similar courses I studied before.
I believe I will be satisfied with my final grade in the course.
I am satisfied with how I am able to apply what I have learned in this
course.
I am satisfied enough with the implementation of BL to recommend it
to others.
Compared to face-to-face course settings, I am less satisfied with BL
experience.
I enjoy working on assignments by myself.

Section: Instructor

This section will ask you about your opinion on how you satisfy with the implementation of BL in terms of
instructor.

                                                                           Strongly                                Strongly
Satisfaction Domain                                                                   Disagree   Neutral   Agree
                                                                           Disagree                                 Agree
Instructor                                                                    1          2         3         4        5
The instructor makes me feel that I am a true member of the class.

I am satisfied with the availability of the instructor(lecturer).

The instructor uses blended learning technology appropriately.

I understand the assignments given by the instructor(lecturer).
Feedback on evaluation of tests and other assignments was given in a
timely manner.
The instructor(lecturer) give enough time for students to complete the
given assignments.

Section: Course Management

This section will ask you about your opinion on how you satisfy with the implementation of BL in terms of
course management.

                                                                           Strongly                                Strongly
Satisfaction Domain                                                                   Disagree   Neutral   Agree
                                                                           Disagree                                 Agree
Course Management                                                             1          2         3         4        5
Discipline is highly observed when the lecturer is not even in the class
session.
The lecturer/supervisor always takes attendance.
Using blended learning was an effective way to learn about the
assigned topics.
Applying blended learning increase my understanding towards the
course that I attend.
Learning activities and assignments of this course by using blended
learning meet my learning expectations.

Section: Technology

This section will ask you about your opinion on how you satisfy with the implementation of BL in terms of
technology.

                                                                           Strongly                                Strongly
Satisfaction Domain                                                                   Disagree   Neutral   Agree
                                                                           Disagree                                 Agree
Technology                                                                    1          2         3         4        5
The content given is clear and comprehensive when the lecturer is not
even in the class session.
Technical problems are not frequent, and they do not adversely affect
my understanding of the course.

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The technology used for blended teaching is reliable.

Better accessibility of educational materials.

Faster and better access to information.

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